[Lung cancer. Developments, concepts and preview of the new WHO classification]

Spectral CT Imaging of Lung Cancer: Quantitative Analysis of Spectral Parameters and Their Correlation with Tumor Characteristics

RATIONALE AND OBJECTIVES

Spectral computed tomography (CT) imaging is widely used in the diagnosis of various cancers. This study aimed to analyze the characteristics of lung squamous cell carcinoma (SC) and adenocarcinoma (AC) using spectral CT imaging.

METHODS

Sixty patients who were examined via spectral CT imaging and confirmed as having AC or SC via surgery and pathology were enrolled in this research project. A spectrum CT scanner was used, and both plain and enhanced CT scans were conducted to acquire spectral images. All patients' samples were used to detect the expression of thyroid transcription factor-1 (TTF-1) and epidermal growth factor receptor (EGFR) in cancer cells via immunohistochemical methods.

RESULTS

Among the 27 cases with AC, 18 cases were identified as TTF-1 positive, 9 cases were found to be TTF-1 negative, 20 cases were confirmed as EGFR positive, and 7 cases were found to be EGFR negative. Among the 33 patients with SC, 6 cases were identified as TTF-1 positive, 27 cases were found to be TTF-1 negative, 19 cases were confirmed as EGFR positive, and 14 cases were found to be EGFR negative. No statistically significant differences were observed in normalized iodine concentration (NIC), K values, and calcium content between the TTF-1-positive and TTF-1-negative groups when considering patients. Statistically significant differences in NIC and K values were noted between the EGFR-positive and EGFR-negative groups among patients with AC, but no such difference was observed regarding calcium content. Significant differences in NIC, K values, and calcium content were observed between the EGFR-positive and EGFR-negative groups among patients with SC.

CONCLUSIONS

In lung cancer cells, the parameters of spectral CT imaging, including NIC and K values, reflect the microvessel density and blood supply. Calcium content is an indicator of the growth status of lung SC.

Development and validation of two prognostic nomograms for predicting survival in patients with non-small cell and small cell lung cancer

Purpose

This study aimed to construct two prognostic nomograms to predict survival in patients with non-small-cell lung cancer (NSCLC) and small-cell lung cancer (SCLC) using a novel set of clinical parameters.

Patients and Methods

Two nomograms were developed, using a retrospective analysis of 5384 NSCLC and 647 SCLC patients seen during a 10-year period at Xiang Ya Affiliated Cancer Hospital (Changsha, China). The patients were randomly divided into training and validation cohorts. Univariate and multivariate analyses were used to identify the prognostic factors needed to establish nomograms for the training cohort. The model was internally validated via bootstrap resampling and externally certified using the validation cohort. Predictive accuracy and discriminatory capability were estimated using concordance index (C-index), calibration curves, and risk group stratification.

Results

The largest contributor to overall survival (OS) prognosis in the NSCLC nomogram was the therapeutic regimen and diagnostic method parameters, and in the SCLC nomogram was the therapeutic regimen and health insurance plan parameters. Calibration curves for the nomogram prediction and the actual observation were in optimal agreement for the 3-year OS and acceptable agreement for the 5-year OS in both training datasets. The C-index was higher for the NSCLC cohort nomogram than for the TNM staging system (0.67 vs. 0.64, P = 0.01) and higher for the SCLC nomogram than for the clinical staging system (limited vs. extensive) (0.60 vs. 0.53, P = 0.12).

Conclusion

Treatment regimen parameter made the largest contribution to OS prognosis in both nomograms, and these nomograms might provide clinicians and patients a simple tool that improves their ability to accurately estimate survival based on individual patient parameters rather than using an averaged predefined treatment regimen.

[Diagnosis, prognosis, and prediction of non-small cell lung cancer. Importance of morphology, immunohistochemistry and molecular pathology]

Tumor diagnostics are based on histomorphology, immunohistochemistry and molecular pathological analysis of mutations, translocations and amplifications which are of diagnostic, prognostic and/or predictive value. In recent decades only histomorphology was used to classify lung cancer as either small (SCLC) or non-small cell lung cancer (NSCLC), although NSCLC was further subdivided in different entities; however, as no specific therapy options were available classification of specific subtypes was not clinically meaningful. This fundamentally changed with the discovery of specific molecular alterations in adenocarcinoma (ADC), e.g. mutations in KRAS, EGFR and BRAF or translocations of the ALK and ROS1 gene loci, which now form the basis of targeted therapies and have led to a significantly improved patient outcome. The diagnostic, prognostic and predictive value of imaging, morphological, immunohistochemical and molecular characteristics as well as their interaction were systematically assessed in a large cohort with available clinical data including patient survival. Specific and sensitive diagnostic markers and marker panels were defined and diagnostic test algorithms for predictive biomarker assessment were optimized. It was demonstrated that the semi-quantitative assessment of ADC growth patterns is a stage-independent predictor of survival and is reproducibly applicable in the routine setting. Specific histomorphological characteristics correlated with computed tomography (CT) imaging features and thus allowed an improved interdisciplinary classification, especially in the preoperative or palliative setting. Moreover, specific molecular characteristics, for example BRAF mutations and the proliferation index (Ki-67) were identified as clinically relevant prognosticators. Comprehensive clinical, morphological, immunohistochemical and molecular assessment of NSCLCs allow an optimized patient stratification. Respective algorithms now form the backbone of the 2015 lung cancer World Health Organization (WHO) classification.

Combination of body mass index and oxidized low density lipoprotein receptor 1 in prognosis prediction of patients with squamous non-small cell lung cancer

Lung cancer, especially non-small cell lung cancer (NSCLC), represents enormous challenges in continuously achieving treatment improvements. Besides cancer, obesity is becoming ever more prevalent. Obesity is increasingly acknowledged as a major risk factor for several types of common cancers. Significant mechanisms overlap in the pathobiology of obesity and tumorigenesis. One of these mechanisms involves oxidized low density lipoprotein receptor 1 (OLR1), as a link between obesity and cancer. Additionally, body mass index (BMI) has been widely used in exploiting the role of obesity on a series of diseases, including cancer. Significantly, squamous NSCLC revealed to be divergent clinical and molecular phenotypes compared with non-squamous NSCLC. Consequently, OLR1 immunostaining score and BMI were assessed by Fisher's linear discriminant analysis to discriminate if progression-free survival (PFS) would exceed 2 years. In addition, the final model was utilized to calculate the discriminant score in each study participant. Finally, 131 patients with squamous NCSLC were eligible for analysis. And a prediction model was established for PFS based on these 2 markers and validated in a second set of squamous NCSLC patients. The model offers a novel tool for survival prediction and could establish a framework for future individualized therapy for patients with squamous NCSLC.

Modified inflammation-based score as an independent malignant predictor in patients with pulmonary focal ground-glass opacity: a propensity score matching analysis

Pulmonary focal Ground-glass Opacities (fGGOs) would frequently be identified after widely implementation of low-dose computed tomography (LDCT) screening. Because of the high false-positive rate of LDCT, antibiotics should be regarded as advocates in clinical management for detected fGGOs. Retrospectively review consecutive patients with fGGOs between August 2006 and August 2012. Then, relative Glasgow prognostic score (GPS) were constructed in three different systems, traditional GPS system (tGPS), modified GPS system 1 (m1GPS), and modified GPS system 2 (m2GPS). Moreover, propensity score matching (PSM) was employed in balancing baseline covariates. After PSM, patients were matched and included in benign and malignant groups as 1:1 ratio. All reported parameters were balanced in both groups and no statistical differences could be detected. Finally, m1GPS exhibited remarkable different distribution between benign and malignant fGGOs. In detail, m1GPS 1 was more frequently observed in benign fGGOs nodules, while m1GPS 2 in malignant fGGOs nodules. Modified inflammation-based score was identified as an independent predictor of malignancies in patients with pulmonary fGGOs. Patients with m1GPS 1 were more likely to be benign fGGOs, while victims with m1GPS 2 more likely to be malignant.

Nomogram to Predict Occult N2 Lymph Nodes Metastases in Patients With Squamous Nonsmall Cell Lung Cancer

For nonsmall cell lung cancer (NSCLC) patients without distant metastases, occult involvement of N2 lymph nodes would be of the utmost importance in determining both treatment and survival. The key to optimal treatment strategies relied on accurate diagnosis, in particular accurate clinical tumor staging. Patients with clinical N0 or N1 staging preoperatively had a sizeable risk to have occult N2 lymph nodes metastases.From November 2004 to March 2007, the entire database in a tertiary hospital of all patients with a pathologic diagnosis of squamous NSCLC underwent anatomical pulmonary resection and systematic mediastinal lymph node dissection were retrospectively collected and reviewed. A nomogram was developed on the basis of a multivariable logistic regression model with a combination of all potential variables. In order to surmount the potential of overestimating predictive performance, both bootstrapping for internal validation and an independent external validation set were employed.A nomogram incorporating the significant risk factors was created to predict the probability of occult N2 lymph nodes metastases. The calibration plot for the probability of occult N2 lymph nodes metastases showed an optimal agreement between the predicted probabilities by nomogram and actual observed probabilities. An objective and accurate nomogram predictive model for occult N2 lymph nodes metastases was drawn up and validated internally and externally in patients with squamous NSCLC.The nomogram model, as a robust tool in predicting occult N2 lymph nodes involvement, could be involved in a cost-effective application of specific diagnostic and therapeutic strategies.